Modeling multi-typed structurally viewed chemicals with the UMLS Refined Semantic Network.
OBJECTIVE
Chemical concepts assigned multiple "Chemical Viewed Structurally" semantic types (STs) in the Unified Medical Language System (UMLS) are subject to ambiguous interpretation. The multiple assignments may denote the fact that a specific represented chemical (combination) is a conjugate, derived via a chemical reaction of chemicals of the different types, or a complex, composed of a mixture of such chemicals. The previously introduced Refined Semantic Network (RSN) is modified to properly model these varied multi-typed chemical combinations.


METHODS
The RSN was previously introduced as an enhanced abstraction of the UMLS's concepts. It features new types, called intersection semantic types (ISTs), each of which explicitly captures a unique combination of ST assignments in one abstract unit. The ambiguous ISTs of different "Chemical Viewed Structurally" ISTs of the RSN are replaced with two varieties of new types, called conjugate types and complex types, which explicitly denote the nature of the chemical interactions. Additional semantic relationships help further refine that new portion of the RSN rooted at the ST "Chemical Viewed Structurally."


METHODS
The number of new conjugate and complex types and the amount of changes to the type assignment of chemical concepts are presented.


RESULTS
The modified RSN, consisting of 35 types and featuring 22 new conjugate and complex types, is presented. A total of 800 (about 98%) chemical concepts representing multi-typed chemical combinations from "Chemical Viewed Structurally" STs are uniquely assigned one of the new types. An additional benefit is the identification of a number of illegal ISTs and ST assignment errors, some of which are direct violations of exclusion rules defined by the UMLS Semantic Network.


CONCLUSIONS
The modified RSN provides an enhanced abstract view of the UMLS's chemical content. Its array of conjugate and complex types provides a more accurate model of the variety of combinations involving chemicals viewed structurally. This framework will help streamline the process of type assignments for such chemical concepts and improve user orientation to the richness of the chemical content of the UMLS.
Chen
Ling
L
Department of Science, BMCC, City University of New York, New York, NY, USA.
Morrey
C Paul
CP
Gu
Huanying
H
Halper
Michael
M
Perl
Yehoshua
Y
D000596:Amino Acids / Q000737:chemistry / Q000145:classification; D003663:Decision Trees; D015394:Molecular Structure; D009930:Organic Chemicals / Q000737:chemistry / Q000145:classification*; D010455:Peptides / Q000737:chemistry / Q000145:classification; D011506:Proteins / Q000737:chemistry / Q000145:classification; D012660:Semantics; D013256:Steroids / Q000737:chemistry / Q000145:classification; D017432:Unified Medical Language System
D000596:Amino Acids; D009930:Organic Chemicals; D010455:Peptides; D011506:Proteins; D013256:Steroids